Paul Brinkman1, Ariane H Wagener2, Pieter-Paul Hekking2, Aruna T Bansal3, Anke-Hilse Maitland-van der Zee2, Yuanyue Wang4, Hans Weda4, Hugo H Knobel5, Teunis J Vink4, Nicholas J Rattray6, Arnaldo D'Amico7, Giorgio Pennazza8, Marco Santonico8, Diane Lefaudeux9, Bertrand De Meulder9, Charles Auffray9, Per S Bakke10, Massimo Caruso11, Pascal Chanez12, Kian F Chung13, Julie Corfield14, Sven-Erik Dahlén15, Ratko Djukanovic16, Thomas Geiser17, Ildiko Horvath18, Nobert Krug19, Jacek Musial20, Kai Sun21, John H Riley22, Dominic E Shaw23, Thomas Sandström24, Ana R Sousa22, Paolo Montuschi25, Stephen J Fowler26, Peter J Sterk2. 1. Department of Respiratory Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands. Electronic address: p.brinkman@amc.uva.nl. 2. Department of Respiratory Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands. 3. Acclarogen, St John's Innovation Centre, Cambridge, United Kingdom. 4. Philips Research, Eindhoven, The Netherlands. 5. Philips Lighting, Eindhoven, The Netherlands. 6. Department of Environmental Health Sciences, Yale School of Public Health, Yale University, New Haven, Conn. 7. Department of Electronic Engineering, University of Rome "Tor Vergata," Rome, Italy. 8. Center for Integrated Research-CIR, Unit for Electronics for Sensor Systems, Campus Bio-Medico U, Rome, Italy. 9. European Institute for Systems Biology and Medicine, CIRI UMR5308, CNRS-ENS-UCBL-INSERM, Lyon, France. 10. Institute of Medicine, University of Bergen, Bergen, Norway. 11. Department of Clinical and Experimental Medicine Hospital University, University of Catania, Catania, Italy. 12. Département des Maladies Respiratoires APHM,U1067 INSERM, Aix Marseille Université Marseille, Marseille, Italy. 13. National Heart and Lung Institute, Imperial College, London, UK Biomedical Research Unit, Royal Brompton & Harefield NHS Trust, London, United Kingdom. 14. AstraZeneca R&D, Mölndal, Sweden; Areteva R&D, Nottingham, United Kingdom. 15. Centre for Allergy Research, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden. 16. NIHR Southampton Respiratory Biomedical Research Unit, Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton, United Kingdom. 17. the Department of Pulmonary Medicine, University Hospital Bern, Bern, Switzerland. 18. Department of Pulmonology, Semmelweis University, Budapest, Hungary. 19. Fraunhofer Institute for Toxicology and Experimental Medicine Hannover, Hannover, Germany. 20. Department of Medicine, Jagiellonian University Medical College, Krakow, Poland. 21. Data Science Institute, South Kensington Campus, Imperial College Londont, London, United Kingdom. 22. Respiratory Therapeutic Unit, GlaxoSmithKline, Stockley Park, United Kingdom. 23. Respiratory Research Unit, University of Nottingham, Nottingham, United Kingdom. 24. Department of Public Health and Clinical Medicine, Department of Medicine, Respiratory Medicine Unit, Umeå University, Umeå, Sweden. 25. Department of Pharmacology, Faculty of Medicine, Catholic University of the Sacred Heart, Rome, Italy. 26. Respiratory Research Group, Faculty of Medical and Human Sciences, University of Manchester, Manchester Academic Healthy Science Centre, and NIHR Translational Research Faculty in Respiratory Medicine, University Hospital of South Manchester, Manchester, United Kingdom; Lancashire Teaching Hospitals NHS Foundation Trust, Preston, United Kingdom.
Abstract
BACKGROUND: Severe asthma is a heterogeneous condition, as shown by independent cluster analyses based on demographic, clinical, and inflammatory characteristics. A next step is to identify molecularly driven phenotypes using "omics" technologies. Molecular fingerprints of exhaled breath are associated with inflammation and can qualify as noninvasive assessment of severe asthma phenotypes. OBJECTIVES: We aimed (1) to identify severe asthma phenotypes using exhaled metabolomic fingerprints obtained from a composite of electronic noses (eNoses) and (2) to assess the stability of eNose-derived phenotypes in relation to within-patient clinical and inflammatory changes. METHODS: In this longitudinal multicenter study exhaled breath samples were taken from an unselected subset of adults with severe asthma from the U-BIOPRED cohort. Exhaled metabolites were analyzed centrally by using an assembly of eNoses. Unsupervised Ward clustering enhanced by similarity profile analysis together with K-means clustering was performed. For internal validation, partitioning around medoids and topological data analysis were applied. Samples at 12 to 18 months of prospective follow-up were used to assess longitudinal within-patient stability. RESULTS: Data were available for 78 subjects (age, 55 years [interquartile range, 45-64 years]; 41% male). Three eNose-driven clusters (n = 26/33/19) were revealed, showing differences in circulating eosinophil (P = .045) and neutrophil (P = .017) percentages and ratios of patients using oral corticosteroids (P = .035). Longitudinal within-patient cluster stability was associated with changes in sputum eosinophil percentages (P = .045). CONCLUSIONS: We have identified and followed up exhaled molecular phenotypes of severe asthma, which were associated with changing inflammatory profile and oral steroid use. This suggests that breath analysis can contribute to the management of severe asthma.
BACKGROUND: Severe asthma is a heterogeneous condition, as shown by independent cluster analyses based on demographic, clinical, and inflammatory characteristics. A next step is to identify molecularly driven phenotypes using "omics" technologies. Molecular fingerprints of exhaled breath are associated with inflammation and can qualify as noninvasive assessment of severe asthma phenotypes. OBJECTIVES: We aimed (1) to identify severe asthma phenotypes using exhaled metabolomic fingerprints obtained from a composite of electronic noses (eNoses) and (2) to assess the stability of eNose-derived phenotypes in relation to within-patient clinical and inflammatory changes. METHODS: In this longitudinal multicenter study exhaled breath samples were taken from an unselected subset of adults with severe asthma from the U-BIOPRED cohort. Exhaled metabolites were analyzed centrally by using an assembly of eNoses. Unsupervised Ward clustering enhanced by similarity profile analysis together with K-means clustering was performed. For internal validation, partitioning around medoids and topological data analysis were applied. Samples at 12 to 18 months of prospective follow-up were used to assess longitudinal within-patient stability. RESULTS: Data were available for 78 subjects (age, 55 years [interquartile range, 45-64 years]; 41% male). Three eNose-driven clusters (n = 26/33/19) were revealed, showing differences in circulating eosinophil (P = .045) and neutrophil (P = .017) percentages and ratios of patients using oral corticosteroids (P = .035). Longitudinal within-patient cluster stability was associated with changes in sputum eosinophil percentages (P = .045). CONCLUSIONS: We have identified and followed up exhaled molecular phenotypes of severe asthma, which were associated with changing inflammatory profile and oral steroid use. This suggests that breath analysis can contribute to the management of severe asthma.
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